扫一扫
关注中图网
官方微博
¥36.4(8.5折)?
预估到手价是按参与促销活动、以最优惠的购买方案计算出的价格(不含优惠券部分),仅供参考,未必等同于实际到手价。
促销活动:
本类五星书更多>
-
>
决战行测5000题(言语理解与表达)
-
>
软件性能测试.分析与调优实践之路
-
>
第一行代码Android
-
>
深度学习
-
>
Unreal Engine 4蓝图完全学习教程
-
>
深入理解计算机系统-原书第3版
-
>
Word/Excel PPT 2013办公应用从入门到精通-(附赠1DVD.含语音视频教学+办公模板+PDF电子书)
Python高维数据分析 版权信息
- ISBN:9787560655772
- 条形码:9787560655772 ; 978-7-5606-5577-2
- 装帧:一般胶版纸
- 册数:暂无
- 重量:暂无
- 所属分类:>
Python高维数据分析 内容简介
本书介绍了矩阵计算的基本方法, 并从特征值分解和奇异值分解出发, 给出一个超定矩阵的*小二乘法问题的模型建立、证明和一般求解方法, 并引出欠秩的多元线性方程组的求解方法问题, 然后介绍了两种有损的降维方法。
Python高维数据分析 目录
Chapter 1 Basis of Matrix Calculation
1.1 Fundamental Concepts
1.1.1 Notation
1.1.2 “BiggerBlock” Interpretations of Matrix Multiplication
1.1.3 Fundamental Linear Algebra
1.1.4 Four Fundamental Subspaces of a Matrix
1.1.5 Vector Norms
1.1.6 Determinants
1.1.7 Properties of Determinants
1.2 The Most Basic Matrix Decomposition
1.2.1 Gaussian Elimination
1.2.2 The LU Decomposition
1.2.3 The LDM Factorization
1.2.4 The LDL Decomposition for Symmetric Matrices
1.2.5 Cholesky Decomposition
1.2.6 Applications and Examples of the Cholesky Decomposition
1.2.7 Eigendecomposition
1.2.8 Matrix Norms
1.2.9 Covariance Matrices
1.3 Singular Value Decomposition (SVD)
1.3.1 Orthogonalization
1.3.2 Existence Proof of the SVD
1.3.3 Partitioning the SVD
1.3.4 Properties and Interpretations of the SVD
1.3.5 Relationship between SVD and ED
1.3.6 Ellipsoidal Interpretation of the SVD
1.3.7 An Interesting Theorem
1.4 The Quadratic Form
1.4.1 Quadratic Form Theory
1.4.2 The Gaussian MultiVariate Probability Density Function
1.4.3 The Rayleigh Quotient
Chapter 2 The Solution of Least Squares Problems
2.1 Linear Least Squares Estimation
2.1.1 Example: Autoregressive Modelling
2.1.2 The LeastSquares Solution
2.1.3 Interpretation of the Normal Equations
2.1.4 Properties of the LS Estimate
2.1.5 Linear LeastSquares Estimation and the Cramer Rao Lower Bound
2.2 A Generalized “PseudoInverse” Approach to Solving the Leastsquares Problem
2.2.1 Least Squares Solution Using the SVD
2.2.2 Interpretation of the PseudoInverse
Chapter 3 Principal Component Analysis
3.1 Introductory Example
3.2 Theory
3.2.1 Taking Linear Combinations
3.2.2 Explained Variation
3.2.3 PCA as a Model
3.2.4 Taking More Components
3.3 History of PCA
3.4 Practical Aspects
3.4.1 Preprocessing
3.4.2 Choosing the Number of Components
3.4.3 When Using PCA for Other Purposes
3.4.4 Detecting Outliers
References
3.5 Sklearn PCA
3.5.1 Source Code
3.5.2 Examples
3.6 Principal Component Regression
3.6.1 Source Code
3.6.2 KFold CrossValidation
3.6.3 Examples
3.7 Subspace Methods for Dynamic Model Estimation in PAT Applications
3.7.1 Introduction
3.7.2 Theory
3.7.3 State Space Models in Chemometrics
3.7.4 Milk Coagulation Monitoring
3.7.5 State Space Based Monitoring
3.7.6 Results
3.7.7 Concluding remarks
3.7.8 Appendix
References
Chapter 4 Partial Least Squares Analysis
4.1 Basic Concept
4.1.1 Partial Least Squares
4.1.2 Form of Partial Least Squares
4.1.3 PLS Regression
4.1.4 Statistic
Reference
4.2 NIPALS and SIMPLS Algorithm
4.2.1 NIPALS
4.2.2 SIMPLS
References
4.3 Programming Method of Standard Partial Least Squares
4.3.1 Crossvalidation
4.3.2 Procedure of NIPALS
4.4 Example Application
4.4.1 Demo of PLS
4.4.2 Corn Dataset
4.4.3 Wheat Dataset
4.4.4 Pharmaceutical Tablet Dataset
4.5 Stack Partial Least Squares
4.5.1 Introduction
4.5.2 Theory of Stack Partial Least Squares
4.5.3 Demo of SPLS
4.5.4 Experiments
References
Chapter 5 Regularization
5.1 Regularization
5.1.1 Classification
5.1.2 Tikhonov Regularization
5.1.3 Regularizers for Sparsity
5.1.4 Other Uses of Regularization in Statistics and Machine Learning
5.2 Ridge Regression: Biased Estimation for Nonorthogonal Problems
5.2.1 Properties of Best Linear Unbiased Estimation
5.2.2 Ridge Regression
5.2.3 The Ridge Trace
5.2.4 Mean Square Error Properties of Ridge Regression
5.2.5 A General Form of Ridge Regression
5.2.6 Relation to Other Work in Regression
5.2.7 Selecting a Better Estimate of ?
References
5.3 Lasso
5.3.1 Introduction
5.3.2 Theory of the Lasso
References
5.4 The Example of Ridge Regression and Lasso Regression
5.4.1 Example
5.4.2 Practical Example
5.5 Sparse PCA
5.5.1 Introduction
5.5.2 Motivation and Method Details
5.5.3 SPCA for p ≥ n and Gene Expression Arrays
5.5.4 Demo of SPCA
References
Chapter 6 Transfer Method
6.1 Calibration Transfer of Spectral Models[1]
6.1.1 Introduction
6.1.2 Calibration Transfer Setting
6.1.3 Related Work
6.1.4 New or Adapted Methods
6.1.5 Standardfree Alternatives to Methods Requiring Transfer StandardsReferences
6.2 PLS Subspace Based Calibration Transfer for NIR Quantitative Analysis
6.2.1 Calibration Transfer Method
6.2.2 Experimental
6.2.3 Results and Discussion
6.2.4 Conclusion
References
6.3 Calibration Transfer Based on Affine Invariance for NIR without Standard Samples
6.3.1 Theory
6.3.2 Experimental
6.3.3 Results and Discussion
6.3.4 Conclusions
1.1 Fundamental Concepts
1.1.1 Notation
1.1.2 “BiggerBlock” Interpretations of Matrix Multiplication
1.1.3 Fundamental Linear Algebra
1.1.4 Four Fundamental Subspaces of a Matrix
1.1.5 Vector Norms
1.1.6 Determinants
1.1.7 Properties of Determinants
1.2 The Most Basic Matrix Decomposition
1.2.1 Gaussian Elimination
1.2.2 The LU Decomposition
1.2.3 The LDM Factorization
1.2.4 The LDL Decomposition for Symmetric Matrices
1.2.5 Cholesky Decomposition
1.2.6 Applications and Examples of the Cholesky Decomposition
1.2.7 Eigendecomposition
1.2.8 Matrix Norms
1.2.9 Covariance Matrices
1.3 Singular Value Decomposition (SVD)
1.3.1 Orthogonalization
1.3.2 Existence Proof of the SVD
1.3.3 Partitioning the SVD
1.3.4 Properties and Interpretations of the SVD
1.3.5 Relationship between SVD and ED
1.3.6 Ellipsoidal Interpretation of the SVD
1.3.7 An Interesting Theorem
1.4 The Quadratic Form
1.4.1 Quadratic Form Theory
1.4.2 The Gaussian MultiVariate Probability Density Function
1.4.3 The Rayleigh Quotient
Chapter 2 The Solution of Least Squares Problems
2.1 Linear Least Squares Estimation
2.1.1 Example: Autoregressive Modelling
2.1.2 The LeastSquares Solution
2.1.3 Interpretation of the Normal Equations
2.1.4 Properties of the LS Estimate
2.1.5 Linear LeastSquares Estimation and the Cramer Rao Lower Bound
2.2 A Generalized “PseudoInverse” Approach to Solving the Leastsquares Problem
2.2.1 Least Squares Solution Using the SVD
2.2.2 Interpretation of the PseudoInverse
Chapter 3 Principal Component Analysis
3.1 Introductory Example
3.2 Theory
3.2.1 Taking Linear Combinations
3.2.2 Explained Variation
3.2.3 PCA as a Model
3.2.4 Taking More Components
3.3 History of PCA
3.4 Practical Aspects
3.4.1 Preprocessing
3.4.2 Choosing the Number of Components
3.4.3 When Using PCA for Other Purposes
3.4.4 Detecting Outliers
References
3.5 Sklearn PCA
3.5.1 Source Code
3.5.2 Examples
3.6 Principal Component Regression
3.6.1 Source Code
3.6.2 KFold CrossValidation
3.6.3 Examples
3.7 Subspace Methods for Dynamic Model Estimation in PAT Applications
3.7.1 Introduction
3.7.2 Theory
3.7.3 State Space Models in Chemometrics
3.7.4 Milk Coagulation Monitoring
3.7.5 State Space Based Monitoring
3.7.6 Results
3.7.7 Concluding remarks
3.7.8 Appendix
References
Chapter 4 Partial Least Squares Analysis
4.1 Basic Concept
4.1.1 Partial Least Squares
4.1.2 Form of Partial Least Squares
4.1.3 PLS Regression
4.1.4 Statistic
Reference
4.2 NIPALS and SIMPLS Algorithm
4.2.1 NIPALS
4.2.2 SIMPLS
References
4.3 Programming Method of Standard Partial Least Squares
4.3.1 Crossvalidation
4.3.2 Procedure of NIPALS
4.4 Example Application
4.4.1 Demo of PLS
4.4.2 Corn Dataset
4.4.3 Wheat Dataset
4.4.4 Pharmaceutical Tablet Dataset
4.5 Stack Partial Least Squares
4.5.1 Introduction
4.5.2 Theory of Stack Partial Least Squares
4.5.3 Demo of SPLS
4.5.4 Experiments
References
Chapter 5 Regularization
5.1 Regularization
5.1.1 Classification
5.1.2 Tikhonov Regularization
5.1.3 Regularizers for Sparsity
5.1.4 Other Uses of Regularization in Statistics and Machine Learning
5.2 Ridge Regression: Biased Estimation for Nonorthogonal Problems
5.2.1 Properties of Best Linear Unbiased Estimation
5.2.2 Ridge Regression
5.2.3 The Ridge Trace
5.2.4 Mean Square Error Properties of Ridge Regression
5.2.5 A General Form of Ridge Regression
5.2.6 Relation to Other Work in Regression
5.2.7 Selecting a Better Estimate of ?
References
5.3 Lasso
5.3.1 Introduction
5.3.2 Theory of the Lasso
References
5.4 The Example of Ridge Regression and Lasso Regression
5.4.1 Example
5.4.2 Practical Example
5.5 Sparse PCA
5.5.1 Introduction
5.5.2 Motivation and Method Details
5.5.3 SPCA for p ≥ n and Gene Expression Arrays
5.5.4 Demo of SPCA
References
Chapter 6 Transfer Method
6.1 Calibration Transfer of Spectral Models[1]
6.1.1 Introduction
6.1.2 Calibration Transfer Setting
6.1.3 Related Work
6.1.4 New or Adapted Methods
6.1.5 Standardfree Alternatives to Methods Requiring Transfer StandardsReferences
6.2 PLS Subspace Based Calibration Transfer for NIR Quantitative Analysis
6.2.1 Calibration Transfer Method
6.2.2 Experimental
6.2.3 Results and Discussion
6.2.4 Conclusion
References
6.3 Calibration Transfer Based on Affine Invariance for NIR without Standard Samples
6.3.1 Theory
6.3.2 Experimental
6.3.3 Results and Discussion
6.3.4 Conclusions
展开全部
书友推荐
- >
朝闻道
朝闻道
¥13.7¥23.8 - >
史学评论
史学评论
¥13.9¥42.0 - >
中国人在乌苏里边疆区:历史与人类学概述
中国人在乌苏里边疆区:历史与人类学概述
¥26.0¥48.0 - >
新文学天穹两巨星--鲁迅与胡适/红烛学术丛书(红烛学术丛书)
新文学天穹两巨星--鲁迅与胡适/红烛学术丛书(红烛学术丛书)
¥11.5¥23.0 - >
二体千字文
二体千字文
¥14.4¥40.0 - >
莉莉和章鱼
莉莉和章鱼
¥17.6¥42.0 - >
企鹅口袋书系列·伟大的思想20:论自然选择(英汉双语)
企鹅口袋书系列·伟大的思想20:论自然选择(英汉双语)
¥6.4¥14.0 - >
有舍有得是人生
有舍有得是人生
¥16.4¥45.0
本类畅销
-
详解Spring Boot(从入门到企业级开发实战)/孙鑫精品图书系列
¥64.2¥129 -
PYTHON应用与实战
¥52.9¥79.8 -
Python编程与数值方法
¥79.6¥109 -
Go语言从入门到项目实战(视频版)
¥60.4¥108 -
软件设计师
¥14.2¥38 -
GO语言编程从入门到实践
¥75.6¥108